论现有环境质量综合指数的可能虚假性

通过各种例子,利用数学分析方法,得出如下结论:①现有的许多环境质量综合指数都存在着可能的虚假性,而这种虚假性是由数学模式本身的性质所决定的;②对于一个数学模式来说,当综合指数一定时,单项指数的值域也是一定的。      

综采工作面粉尘运移和粉尘浓度三维分布的数值模拟研究

分析了综采工作面流场及粉尘分布的特点 ,视工作面含尘风流为气 粒两相流 ,建立了描述综采工作面风流中粉尘运动和浓度分布的数学模型。采用计算流体力学的有限容积法求解 ,编制了模拟计算综采工作面三维空间内风速和粉尘浓度分布的计算机程序     

金川公司二矿区通风系统风流非定常流动规律研究

实际上 ,矿井的风流状态受多种因素影响 ,金川公司二矿区井下风流就处于非定常状态 ,为了保证生产安全 ,控制风流状态 ,采取相应的通风措施 ,开设课题研究 ,建立了井巷内空气非定常流动能量方程和通风网络中非定常流动的数学模型。根据该模型可以模拟出矿井通风系统内非定常状态下任意时刻、任意位置通过风量。通过对金川公司二矿区井下机站风机开停和风门开关引起通风系统风流的非定常流动过程的数值模拟 ,得出了受其影响区域内风流状态的变化规律 ,并为其采取通风措施和控制风流状态 ,提供了科学依据     

Consistent assessments of pathways toward sustainable development and climate stabilization

Many of the numerous difficult issues facing the world today involve relationships entailing trade‐offs and synergies. This study quantitatively assesses some alternative scenarios using integrated assessment models, and provides several indicators relating to sustainable development and climate change, such as indicators of income (per capita GDP), poverty, water stress, food access, sustainable energy use, energy security, and ocean acidification, with high consistencies among the indicators within a scenario. According to the analyses, economic growth helps improve many of the indicators for sustainable development. On the other hand, climate change will induce some severe impacts such as ocean acidification under a non‐climate intervention scenario (baseline scenario). Deep emission reductions, such as to 2°C above the pre‐industrial level, could cause some sustainable development indicators to worsen. There are complex trade‐offs between climate change mitigation levels and several sustainable development indicators. A delicately balanced approach to economic growth will be necessary for sustainable development and responses to climate change.     

Structural equation model analysis for the evaluation of overall driving performance: A driving simulator study focusing on driver distraction

Objective: The present research relies on 2 main objectives. The first is to investigate whether latent model analysis through a structural equation model can be implemented on driving simulator data in order to define an unobserved driving performance variable. Subsequently, the second objective is to investigate and quantify the effect of several risk factors including distraction sources, driver characteristics, and road and traffic environment on the overall driving performance and not in independent driving performance measures.

Methods: For the scope of the present research, 95 participants from all age groups were asked to drive under different types of distraction (conversation with passenger, cell phone use) in urban and rural road environments with low and high traffic volume in a driving simulator experiment. Then, in the framework of the statistical analysis, a correlation table is presented investigating any of a broad class of statistical relationships between driving simulator measures and a structural equation model is developed in which overall driving performance is estimated as a latent variable based on several individual driving simulator measures.

Results: Results confirm the suitability of the structural equation model and indicate that the selection of the specific performance measures that define overall performance should be guided by a rule of representativeness between the selected variables. Moreover, results indicate that conversation with the passenger was not found to have a statistically significant effect, indicating that drivers do not change their performance while conversing with a passenger compared to undistracted driving. On the other hand, results support the hypothesis that cell phone use has a negative effect on driving performance. Furthermore, regarding driver characteristics, age, gender, and experience all have a significant effect on driving performance, indicating that driver-related characteristics play the most crucial role in overall driving performance.

Conclusions: The findings of this study allow a new approach to the investigation of driving behavior in driving simulator experiments and in general. By the successful implementation of the structural equation model, driving behavior can be assessed in terms of overall performance and not through individual performance measures, which allows an important scientific step forward from piecemeal analyses to a sound combined analysis of the interrelationship between several risk factors and overall driving performance.     

Calculating losses of crops in Denmark caused by high ozone levels

In order to help guide air pollution legislation at the European level, harmful air pollution effects on agriculture crops and the consequent economic implications for policy have been studied for more than a decade. Ozone has been labeled as the most serious of the damaging air pollutants to agriculture, where growth rates and consequently yields are dramatically reduced. Quantifying the effects has formed a key factor in policymaking. Based on the widely held view that AOT40 (Accumulated exposure Over Threshold of 40 ppb) is a good indicator of ozone-induced damage, the Danish Eulerian Model (DEM) was used to compute reduced agriculture yields on a 50 km×50 km grid over Europe. In one set of scenarios, a ten year meteorological time series was combined with realistic emission inventories. In another, various idealized emission reduction scenarios are applied to the same meteorological time series. The results show substantial inter-annual variability in economic losses, due in most part to meteorological conditions which varied much more substantially than the emissions during the same period. It is further shown that, taking all uncertainties into account, estimates of ozone-induced economic losses require that a long meteorological record is included in the analysis, for statistical significance to be improved to acceptable levels for use in policy analysis. In this study, calculations were made for Europe as a whole, though this paper presents results relevant for Denmark.     

On the Estimation of Dispersal Kernels from Individual Mark-Recapture Data

We present a new method for estimating a distribution of dispersal displacements (a dispersal kernel) from mark-recapture data. One conventional method of calculating the dispersal kernel assumes that the distribution of displacements are Gaussian (e.g. resulting from a diffusion process) and that individuals remain within sampled areas. The first assumption prohibits an analysis of dispersal data that do not exhibit the Gaussian distribution (a common situation); the second assumption leads to underestimation of dispersal distance because individuals that disperse outside of sampling areas are never recaptured. Our method eliminates these two assumptions. In addition, the method can also accommodate mortality during a sampling period. This new method uses integrodifference equations to express the probability of spatial mark-recapture data; associated dispersal, survival, and recapture parameters are then estimated using a maximum likelihood method. We examined the accuracy of the estimators by applying the method to simulated data sets. Our method suggests designs for future mark-recapture experiments. Received: January 2004 / Revised: July 2005     

A simple approach to modeling microbial biomass in the rhizosphere

Microorganisms make an important contribution to the degradation of contaminants in bioremediation as well as in phytoremediation. An accurate estimation of microbial concentrations in the soil would be valuable in predicting contaminant dissipation during various bioremediation processes. A simple modeling approach to quantify the microbial biomass in the rhizosphere was developed in this study. Experiments were conducted using field column lysimeters planted with Eastern gamagrass. The microbial biomass concentrations from the rhizosphere soil, bulk soil, and unplanted soil were monitored for six months using an incubation–fumigation method. The proposed model was applied to the field microbial biomass data and good correlation between simulated and experimental data was achieved. The results indicate that plants increase microbial concentrations in the soil by providing root exudates as growth substrates for microorganisms. Since plant roots are initially small and do not produce large quantities of exudates when first seeded, the addition of exogenous substrates may be needed to increase initial microbial concentrations at the start of phytoremediation projects.     

Studying crop sequences with CarrotAge,a HMM-based data mining software

We have developed a knowledge discovery system based on high-order hidden Markov models for analyzing spatio-temporal data bases. This system, named CarrotAge , takes as input an array of discrete data – the rows represent the spatial sites and the columns the time slots – and builds a partition together with its a posteriori probability. CarrotAge has been developed for studying the cropping patterns of a territory. It uses therefore an agricultural drench database, named Ter-Uti , which records every year the land-use category of a set of sites regularly spaced. The results of CarrotAge are interpreted by agronomists and used in research works linking agricultural land use and water management. Moreover, CarrotAge can be used to find out and study crop sequences in large territories, that is a main question for agricultural and environmental research, as discussed in this paper.     

Probability distributions and statistical inference for axial data

Observations on axes which lack information on the direction of propagation are referred to as axial data. Such data are often encountered in enviromental sciences, e.g. observations on propagations of cracks or on faults in mining walls. Even though such observations are recorded as angles, circular probability models are inappropriate for such data since the constraint that observations lie only in [0, π) needs to be enforced. Probability models for such axial data are argued here to have a general structure stemming from that of wrapping a circular distribution on a semi-circle. In particular, we consider the most popular circular model, the von Mises or circular normal distribution, and derive the corresponding axial normal distribution. Certain properties of this distribution are established. Maximum likelihood estimation of its parameters are shown to be surprisingly, in contrast to trigonometric moment estimation, numerically quite appealing. Finally we illustrate our results by several real life axial data sets. Received: September 2004/ Revised: December 2004